Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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Updated
Sep 22, 2022 - Jupyter Notebook
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Ontology for the description of human clinical features
An automatic ML model optimization tool.
[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets
Automatic machine learning for tabular data. ⚡🔥⚡
Phenotype comparison tools using semantic similarity.
Bayesian Optimization Hyperband Hyperparameter Optimization
Java library to map LOINC-encoded test results to Human Phenotype Ontology
Python library for extracting HPO encoded phenotypes from text
A Python library to work with, analyze, filter and inspect the Human Phenotype Ontology
Flexible Bayesian Optimization in R
Collections of GA4GH phenopackets that represent individuals with Mendelian diseases.
EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.
Internationalisation of the HPO content
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